= Solution
Using the smaller <logistic regression>, approximate pointwise <Wald confidence intervals> are an estimate plus or minus $1.96$ times its <standard error>:
$$
\boxed{\widehat\beta_0=5.16258,\quad \beta_0\in[2.94717,7.37799];\qquad \widehat\beta_1=-0.08207,\quad \beta_1\in[-0.113332,-0.050808].}
$$
Both intervals rely on the large-sample <normal approximation> and correct model assumptions. The negative slope indicates decreasing purchase probability as price rises. A one-unit price increase multiplies the <odds> by $e^{-0.08207}\simeq0.9212$; it does not subtract a fixed amount from the probability. There is no significant additional layout effect in the previous <likelihood-ratio test>, rather than a demonstration of no effect.
In the selected model layout B has the same prediction rule as the other layouts. At price 100 the <linear predictor> is $5.16258-0.08207(100)=-3.04442$. Applying the inverse <logit link> gives
$$
\boxed{\widehat p(100,B)=\frac{1}{1+e^{3.04442}}\simeq0.04546.}
$$
The intercept refers to price zero; if that lies outside the observed price range, its direct substantive interpretation would require extrapolation.
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